IAM

TAG»CUDA«

ARTICLE

Benchmarking Bit Errors in Quantized Neural Networks with PyTorch

Similar to my article series on adversarial robustness, I was planning to have a series on bit errors robustness accompanied by PyTorch code. Instead, due to time constraints, I decided to condense the information into a single article. The code for the originally planned six articles is available on GitHub.

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JUNE2023

PROJECT

OPEN SOURCE Bit Error Robustness in PyTorch Article Series I was planning to have an article series on bit error robustness in deep learning — similar to my article series on adversarial robustness — with accompanying PyTorch code. However, the recent progress in machine learning made me focus on other projects. Nevertheless, the articles should […]

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NOVEMBER2022

PROJECT

Torch/CUDA implementation of batch normalization for OctNets.

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AUGUST2022

PROJECT

A template for extending PyTorch using C/CUDA operations.

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AUGUST2022

PROJECT

Basic and advanced torch examples, template for implementing custom C/CUDA modules and implementations of variational auto-encoders.

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AUGUST2022

PROJECT

3D mesh fusion, voxelization and evaluation for computer vision research.

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ARTICLE

Implementing Custom PyTorch Tensor Operations in C and CUDA

PyTorch, alongside TensorFlow, has become standard among deep learning researchers and practitioners. While PyTorch provides a large variety in terms of tensor operations or deep learning layers, some specialized operations still need to be implemented manually. In cases where runtime is crucial, this should be done in C or CUDA for supporting both CPU and GPU computation. In this article, I want to provide a simple example and framework for extending PyTorch with custom C and CUDA operations using CFFI for Python and CuPy.

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ARTICLE

Convolutional Batch Normalization for OctNets

During my master thesis I partly worked on OctNets, octree-bases convolutional neural networks for efficient learning in 3D. Among others, I implemented convolutional batch normalization for OctNets. This article briefly discusses the implementation, which will be available on GitHub.

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ARTICLE

Implementing Torch Modules in C/CUDA

Torch is a framework for scientific computing in LUA. However, it has mostly been used for deep learning research as it provides efficient and comfortable C/CUDA implementations of a wide range of (convolutional and/or recurrent) neural network components. In this article, I want to provide a code template allowing to easily extend torch.nn by custom modules implemented in C and/or CUDA without knowledge of Torch’s core.

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ARTICLE

Compiling OpenCV 2.4.x with CUDA 9

Currently, both OpenCV 2 and OpenCV 3 seem to have some minor issues with CUDA 9. However, CUDA 9 is required for the latest generation of NVidia graphics cards. In this article, based on this StackOverflow question, I want to discuss a very simple patch to get OpenCV 2 running with CUDA 9.

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